Despite recent emergence of gut microbiota as a novel factor influencing obesity and related metabolic disorders, such as diabetes and cardiovascular disease, very little is known about the role of long-term diet on microbially mediated disease risk. The primary reasons for this lack of understanding are: 1) lack of high quality, long-term diet data in combination with microbiome data; 2) lack of data in populations undergoing rapid changes in environments and outcomes, with enough variability to create a range of exposures; and 3) insufficient use of complex statistical models to allow examination of each piece of the time-dependent, complex system. The China Health and Nutrition Survey (CHNS), an NIH-funded study of more than 15,000 individuals followed over 25 years, provides high quality longitudinal data and captures the transition from traditional to Western diet in parallel with urbanization and emergence of obesity and cardiovascular disease during the past two decades. We will use these data to generate insights that would not be obtainable in studies with subjects who consume only a Westernized diet. Using sophisticated statistical models we propose to examine whether information on gut microbiota composition along with data on the plasma metabolome can predict markers of cardiometabolic disease (body mass index, central adiposity, diabetes and inflammation), allowing us to implicate microbiota in disease pathways. We propose to collect fecal samples from 10,000 adult CHNS participants aged 30-65 at the 2015 exam. In a subsample from two neighboring Southern provinces (Guizhou, actively urbanizing, n=500; and Hunan, already urbanized, n=500) varying in current (and long-term change) in Western diet, we will create microbial sequences in the 16S V3-V4 region (n=1,000) and store the remaining (n=9,000) fecal samples. We will select a subsample with 16S data who have consumed a traditional diet over the 25 years (n=400) and conduct plasma metabolomics of the host. In the CHNS subsample with microbiota plus metabolome (n=400) data, we will collect replicate blood and fecal samples in CHNS2017 to derive 16S and plasma metabolomics data to assess two-year changes (2015-2017) in diet, gut microbiota, plasma metabolites and in markers of cardiometabolic disease. We will examine whether gut microbiota and plasma metabolites differ depending upon when, within the 25-year period, diet changes occur, and if they are associated with health outcomes. In our longitudinal subsample we will examine changes in markers of Western diet in relation to concurrent changes in microbial diversity and community composition, and metabolites. We first use a series of standard regression models and then high dimensional regression analysis with variable selection and validation to build predictive models. We capitalize upon an established and well-characterized, large cohort with far greater variability in diet and microbial communities than studies in cohorts on only Western diets. The proposed project will substantially transform current understanding of the intersection of diet, gut microbiota, host metabolism and cardiometabolic disease.